
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_csv("hotel_bookings.csv")
df.head(5)


df.shape

(119390, 32)


df.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


df.isnull().values.any()

True


df.isnull().sum()

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64


df.fillna(0, inplace = True)


df.isnull().sum()

hotel                             0
is_canceled                       0
lead_time                         0
arrival_date_year                 0
arrival_date_month                0
arrival_date_week_number          0
arrival_date_day_of_month         0
stays_in_weekend_nights           0
stays_in_week_nights              0
adults                            0
children                          0
babies                            0
meal                              0
country                           0
market_segment                    0
distribution_channel              0
is_repeated_guest                 0
previous_cancellations            0
previous_bookings_not_canceled    0
reserved_room_type                0
assigned_room_type                0
booking_changes                   0
deposit_type                      0
agent                             0
company                           0
days_in_waiting_list              0
customer_type                     0
adr                               0
required_car_parking_spaces       0
total_of_special_requests         0
reservation_status                0
reservation_status_date           0
dtype: int64


df['meal'].value_counts()

meal
BB           92310
HB           14463
SC           10650
Undefined     1169
FB             798
Name: count, dtype: int64


df['children'].value_counts()

children
0.0     110800
1.0       4861
2.0       3652
3.0         76
10.0         1
Name: count, dtype: int64


df['adults'].unique()

array([ 2,  1,  3,  4, 40, 26, 50, 27, 55,  0, 20,  6,  5, 10],
      dtype=int64)


df['babies'].unique()

array([ 0,  1,  2, 10,  9], dtype=int64)


len(df[df['adults'] == 0])

403


len(df[df['babies'] == 0])

118473


filter = (df['children'] == 0) & (df['adults'] == 0) & (df['babies'] == 0)
df[filter]


data = df[~filter]


data.shape

(119210, 32)


df['hotel'].unique()

array(['Resort Hotel', 'City Hotel'], dtype=object)


df.head(10)


data.head(2)


data['hotel'].unique()

array(['Resort Hotel', 'City Hotel'], dtype=object)


data['is_canceled'].unique()

array([0, 1], dtype=int64)


resort = data[(data['hotel'] == 'Resort Hotel') & (data['is_canceled'] == 0)]
City = data[(data['hotel'] == 'City Hotel') & (data['is_canceled'] == 0)]


resort.shape

(28927, 32)


City.shape

(46084, 32)


resort['country'].value_counts()

country
PRT    10184
GBR     5922
ESP     3105
IRL     1734
FRA     1399
       ...  
UGA        1
COM        1
MUS        1
BIH        1
SAU        1
Name: count, Length: 119, dtype: int64


resort['country'].value_counts().index

Index(['PRT', 'GBR', 'ESP', 'IRL', 'FRA', 'DEU',  'CN', 'NLD',     0, 'USA',
       ...
       'MKD', 'SMR', 'BDI', 'SYR', 'CYM', 'UGA', 'COM', 'MUS', 'BIH', 'SAU'],
      dtype='object', name='country', length=119)


lables = resort['country'].value_counts().index
values = resort['country'].value_counts()


import plotly.graph_objs as go
from plotly.offline import iplot
import plotly.express as px


trace=go.Pie(labels=lables, values=values,
               hoverinfo='label+percent', textinfo='value'
               )


iplot([trace])


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


country_wise = data[data['is_canceled'] == 0]['country'].value_counts().reset_index()
country_wise.columns = ['country', 'No of guests']
country_wise


!pip install folium

Requirement already satisfied: folium in c:\users\abhis\anaconda3\lib\site-packages (0.17.0)
Requirement already satisfied: branca>=0.6.0 in c:\users\abhis\anaconda3\lib\site-packages (from folium) (0.7.2)
Requirement already satisfied: jinja2>=2.9 in c:\users\abhis\anaconda3\lib\site-packages (from folium) (3.1.3)
Requirement already satisfied: numpy in c:\users\abhis\anaconda3\lib\site-packages (from folium) (1.26.4)
Requirement already satisfied: requests in c:\users\abhis\anaconda3\lib\site-packages (from folium) (2.31.0)
Requirement already satisfied: xyzservices in c:\users\abhis\anaconda3\lib\site-packages (from folium) (2022.9.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\abhis\anaconda3\lib\site-packages (from jinja2>=2.9->folium) (2.1.3)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\abhis\anaconda3\lib\site-packages (from requests->folium) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in c:\users\abhis\anaconda3\lib\site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\abhis\anaconda3\lib\site-packages (from requests->folium) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\abhis\anaconda3\lib\site-packages (from requests->folium) (2024.6.2)


# map graph
import folium
from folium.plugins import HeatMap


b = folium.Map()


country_wise.dtypes

country         object
No of guests     int64
dtype: object


gueats = px.choropleth(country_wise, 
                      locations = country_wise['country'],
                      color = country_wise['No of guests'],
                      hover_name = country_wise['country'],
                       title = "Home country of gueats"
                      )


gueats.show()


!pip install sort-dataframeby-monthorweek

Requirement already satisfied: sort-dataframeby-monthorweek in c:\users\abhis\anaconda3\lib\site-packages (0.4)


!pip install sorted-months-weekdays

Requirement already satisfied: sorted-months-weekdays in c:\users\abhis\anaconda3\lib\site-packages (0.2)


data.head()


# How much do guests pay for a room per night?
data2 = data[data['is_canceled'] == 0]


data2.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


plt.figure(figsize=(12, 8))
sns.boxplot(x = 'reserved_room_type',
           y = 'adr', 
           hue = 'hotel',
           data = data2)
plt.title("Price of room types per night and person")
plt.xlabel("Room Type")
plt.ylabel("Price [EUR]")
plt.legend(loc = 'upper right')
plt.ylim(0, 600)
plt.show()


# How does the price per night vary over the year?


data_resort = resort[resort['is_canceled'] == 0]


data_city = City[City['is_canceled'] == 0]


data_city


data_resort


resort_hotel = data_resort.groupby(['arrival_date_month'])['adr'].mean().reset_index()
resort_hotel


city_hotel = data_city.groupby(['arrival_date_month'])['adr'].mean().reset_index()
city_hotel


final=resort_hotel.merge(city_hotel,on='arrival_date_month')
final.columns=['month','price_for_resort','price_for_city_hotel']
final


import sort_dataframeby_monthorweek as sd


final = sd.Sort_Dataframeby_Month(df = final, monthcolumnname = 'month')
final


px.line(final, x = 'month', y=['price_for_resort', 'price_for_city_hotel'], title ='Room price per night over the month' )


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


data['market_segment'].value_counts()

market_segment
Online TA        56408
Offline TA/TO    24182
Groups           19791
Direct           12582
Corporate         5282
Complementary      728
Aviation           235
Undefined            2
Name: count, dtype: int64


plt.figure(figsize = (8,8))
sns.boxplot(x = 'market_segment', 
           y = 'stays_in_weekend_nights', 
           hue = 'hotel',
           data = data)

<Axes: xlabel='market_segment', ylabel='stays_in_weekend_nights'>


# Analysing Preference of Guests, what they basically Prefer in meal?


data['meal'].value_counts()

meal
BB           92236
HB           14458
SC           10549
Undefined     1169
FB             798
Name: count, dtype: int64


px.pie(data, names = data['meal'].value_counts().index,
      values = data['meal'].value_counts().values, hole = 0.5)


data.groupby(['hotel', 'meal']).agg({'meal':'count'}).unstack()


# Analyse Special Request done by Cuustomers


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


sns.countplot(x = 'total_of_special_requests', data = data)
plt.title("Total Special Request")

Text(0.5, 1.0, 'Total Special Request')


sns.countplot(x = 'total_of_special_requests', data = data, hue = 'hotel')
plt.title("Total Special Request")

Text(0.5, 1.0, 'Total Special Request')


data.groupby(['total_of_special_requests',
             'is_canceled']).agg({'total_of_special_requests':'count'})


pivot = data.groupby(['total_of_special_requests',
             'is_canceled']).agg({'total_of_special_requests':'count'}).rename(columns = {'total_of_special_requests':'count'}).unstack()
pivot


pivot.plot(kind= 'bar')

<Axes: xlabel='total_of_special_requests'>


# Which are the most busy month or in which months Guests are high?


data_resort.head()


data_resort.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


rush_resort=data_resort['arrival_date_month'].value_counts().reset_index()
rush_resort.columns=['month','no of guests']
rush_resort


rush_city=data_city['arrival_date_month'].value_counts().reset_index()
rush_city.columns=['month','no of guests']
rush_city


final_rush = rush_resort.merge(rush_city, on = 'month')
final_rush.columns = ['month', 'no of guests in resort', 'no of guests in city']
final_rush


final_rush = sd.Sort_Dataframeby_Month(df =final_rush, monthcolumnname = 'month')
final_rush


final_rush.dtypes

month                     object
no of guests in resort     int64
no of guests in city       int64
dtype: object


px.line(data_frame =final_rush, x = 'month', 
       y = ['no of guests in resort', 'no of guests in city'],
       title = 'Total no og gauest per months')


data.head()


filter = data['is_canceled'] == 0
clean_data = data[filter]


clean_data.head()


clean_data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


clean_data['total_nights_spend'] = clean_data['stays_in_weekend_nights'] + clean_data['stays_in_week_nights']

C:\Users\abhis\AppData\Local\Temp\ipykernel_10072\1100290991.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


clean_data.head()


stay = clean_data.groupby(['total_nights_spend', 'hotel']).agg('count').reset_index()
stay = stay.iloc[:, 0:3]
stay.head()


stay = stay.rename(columns = {'is_canceled': 'Number of stay'})
stay.head()


plt.figure(figsize=(20, 8))
sns.barplot(x = 'total_nights_spend', y = 'Number of stay', hue = 'hotel',
           hue_order = ['City Hotel', 'Resort Hotel'], data = stay)

<Axes: xlabel='total_nights_spend', ylabel='Number of stay'>


# Bookings by market segment


clean_data['market_segment'].value_counts()

market_segment
Online TA        35673
Offline TA/TO    15880
Direct           10648
Groups            7697
Corporate         4291
Complementary      639
Aviation           183
Name: count, dtype: int64


fig = px.pie(clean_data,
            values = clean_data['market_segment'].value_counts().values,
            names = clean_data['market_segment'].value_counts().index,
            title = "Booking per market segment")
fig.update_traces(rotation=-90, textinfo="percent+label")
fig.show()


total_cancellations = df['is_canceled'].sum()
print(total_cancellations)

44224


cancellations_by_month = df[df['is_canceled'] == 1].groupby('arrival_date_month').size()
month_with_most_cancellations = cancellations_by_month.idxmax()
print(month_with_most_cancellations)

August

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	7/3/2015

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	2	...	No Deposit	0.0	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	0	737	2015	July	27	1	0	2	...	No Deposit	0.0	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	0	7	2015	July	27	1	1	1	...	No Deposit	0.0	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	0	13	2015	July	27	1	1	1	...	No Deposit	304.0	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	0	14	2015	July	27	1	2	2	...	No Deposit	240.0	Transient	98.0	1	Check-Out	7/3/2015
5	Resort Hotel	0	14	2015	July	27	1	2	2	...	No Deposit	240.0	Transient	98.0	1	Check-Out	7/3/2015
6	Resort Hotel	0	0	2015	July	27	1	2	2	...	No Deposit	0.0	Transient	107.0	0	Check-Out	7/3/2015
7	Resort Hotel	0	9	2015	July	27	1	2	2	...	No Deposit	303.0	Transient	103.0	1	Check-Out	7/3/2015
8	Resort Hotel	1	85	2015	July	27	1	3	2	...	No Deposit	240.0	Transient	82.0	1	Canceled	5/6/2015
9	Resort Hotel	1	75	2015	July	27	1	3	2	...	No Deposit	15.0	Transient	105.5	0	Canceled	4/22/2015

	country	No of guests
0	PRT	20977
1	GBR	9668
2	FRA	8468
3	ESP	6383
4	DEU	6067
...	...	...
161	BHR	1
162	DJI	1
163	MLI	1
164	NPL	1
165	FRO	1

	arrival_date_month	adr
0	April	75.867816
1	August	181.205892
2	December	68.410104
3	February	54.147478
4	January	48.761125
5	July	150.122528
6	June	107.974850
7	March	57.056838
8	May	76.657558
9	November	48.706289
10	October	61.775449
11	September	96.416860

	arrival_date_month	adr
0	April	111.962267
1	August	118.674598
2	December	88.401855
3	February	86.520062
4	January	82.330983
5	July	115.818019
6	June	117.874360
7	March	90.658533
8	May	120.669827
9	November	86.946592
10	October	102.004672
11	September	112.776582

Hotel_Booking_EDA_Analysis¶

Perform Spatial Analysis on Guests Home-Town¶

2. How much do guests pay for a room per night?¶

3. How does the price per night vary over the year?¶

4. Distribution of Nights Spent at Hotels by Market Segment and Hotel Type¶

5. Analysing Preference of Guests, what they basically Prefer in meal?¶

5. Analyse Special Request done by Cuustomers¶

6. Which are the most busy month or in which months Guests are high?¶

6. How long do people stay at the hotels?¶

7. Bookings by market segment¶

Assignment¶

1. How many bookings were cancelled?¶

2. Which month have the highest number of cancellations?¶

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
2224	Resort Hotel	0	1	2015	October	41	6	0	3	0	...	No Deposit	0.0	174.0	0	Transient-Party	0.00	0	0	Check-Out	10/6/2015
2409	Resort Hotel	0	0	2015	October	42	12	0	0	0	...	No Deposit	0.0	174.0	0	Transient	0.00	0	0	Check-Out	10/12/2015
3181	Resort Hotel	0	36	2015	November	47	20	1	2	0	...	No Deposit	38.0	0.0	0	Transient-Party	0.00	0	0	Check-Out	11/23/2015
3684	Resort Hotel	0	165	2015	December	53	30	1	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/4/2016
3708	Resort Hotel	0	165	2015	December	53	30	2	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/5/2016
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
115029	City Hotel	0	107	2017	June	26	27	0	3	0	...	No Deposit	7.0	0.0	0	Transient	100.80	0	0	Check-Out	6/30/2017
115091	City Hotel	0	1	2017	June	26	30	0	1	0	...	No Deposit	0.0	0.0	0	Transient	0.00	1	1	Check-Out	7/1/2017
116251	City Hotel	0	44	2017	July	28	15	1	1	0	...	No Deposit	425.0	0.0	0	Transient	73.80	0	0	Check-Out	7/17/2017
116534	City Hotel	0	2	2017	July	28	15	2	5	0	...	No Deposit	9.0	0.0	0	Transient-Party	22.86	0	1	Check-Out	7/22/2017
117087	City Hotel	0	170	2017	July	30	27	0	2	0	...	No Deposit	52.0	0.0	0	Transient	0.00	0	0	Check-Out	7/29/2017

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
40060	City Hotel	0	6	2015	July	27	1	0	2	1	...	No Deposit	6.0	0.0	0	Transient	0.00	0	0	Check-Out	7/3/2015
40066	City Hotel	0	3	2015	July	27	2	0	3	1	...	No Deposit	1.0	0.0	0	Transient-Party	58.67	0	0	Check-Out	7/5/2015
40070	City Hotel	0	43	2015	July	27	3	0	2	2	...	No Deposit	1.0	0.0	0	Transient-Party	86.00	0	0	Check-Out	7/5/2015
40071	City Hotel	0	43	2015	July	27	3	0	2	2	...	No Deposit	1.0	0.0	0	Transient-Party	43.00	0	0	Check-Out	7/5/2015
40072	City Hotel	0	43	2015	July	27	3	0	2	2	...	No Deposit	1.0	0.0	0	Transient-Party	86.00	0	0	Check-Out	7/5/2015
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
119385	City Hotel	0	23	2017	August	35	30	2	5	2	...	No Deposit	394.0	0.0	0	Transient	96.14	0	0	Check-Out	9/6/2017
119386	City Hotel	0	102	2017	August	35	31	2	5	3	...	No Deposit	9.0	0.0	0	Transient	225.43	0	2	Check-Out	9/7/2017
119387	City Hotel	0	34	2017	August	35	31	2	5	2	...	No Deposit	9.0	0.0	0	Transient	157.71	0	4	Check-Out	9/7/2017
119388	City Hotel	0	109	2017	August	35	31	2	5	2	...	No Deposit	89.0	0.0	0	Transient	104.40	0	0	Check-Out	9/7/2017
119389	City Hotel	0	205	2017	August	35	29	2	7	2	...	No Deposit	9.0	0.0	0	Transient	151.20	0	2	Check-Out	9/7/2017

	meal
meal	BB	FB	HB	SC	Undefined
hotel
City Hotel	62233.0	44.0	6412.0	10474.0	NaN
Resort Hotel	30003.0	754.0	8046.0	75.0	1169.0

		total_of_special_requests
total_of_special_requests	is_canceled
0	0	36667
0	1	33534
1	0	25867
1	1	7316
2	0	10086
2	1	2866
3	0	2049
3	1	445
4	0	304
4	1	36
5	0	38
5	1	2

	month	no of guests
0	August	3257
1	July	3137
2	October	2575
3	March	2571
4	April	2550
5	May	2535
6	February	2308
7	September	2102
8	June	2037
9	December	2014
10	November	1975
11	January	1866

	month	no of guests
0	August	5367
1	July	4770
2	May	4568
3	June	4358
4	October	4326
5	September	4283
6	March	4049
7	April	4010
8	February	3051
9	November	2676
10	December	2377
11	January	2249